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Autonomous Analysis of Curated Patient Data Using a Large Language Model-Based Multiagent Framework.

Jiasheng Wang1, David M Swoboda2, Aziz Nazha3

  • 1Division of Hematology, Department of Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH.

JCO Clinical Cancer Informatics
|December 19, 2025
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Summary
This summary is machine-generated.

A novel multiagent artificial intelligence (AI) framework automates complex medical data analysis, significantly outperforming generalized large language models (LLMs) in accuracy for replicating study outcomes.

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Medicine
  • Data Science

Background:

  • Analyzing complex medical datasets is a specialized and time-consuming task.
  • Current methods often lack efficiency and can be prone to errors.
  • Automating these workflows is crucial for advancing medical research.

Purpose of the Study:

  • To develop and evaluate a novel multiagent artificial intelligence (AI) framework for automating medical data analysis.
  • To compare the performance of this AI framework against non-agent-based approaches, specifically large language models (LLMs).

Main Methods:

  • A six-party AI agent system was developed using the AutoGen platform, with specialized agents for planning, data retrieval, cleaning, statistical analysis, and review, powered by OpenAI gpt-4o.
  • The framework was applied to deidentified single patient-level data sets from 20 bone marrow transplantation studies (2021-2023).
  • Performance was benchmarked against direct use of ChatGPT 4o for replicating published primary outcomes.

Main Results:

  • The multiagent framework successfully replicated 53.3% of primary outcomes, significantly outperforming ChatGPT 4o (35.0%, P = .04).
  • Multiagent framework failures were mainly due to data transformation (46.4%) and analysis code errors (21.4%).
  • ChatGPT 4o failures stemmed from incorrect statistical methods (38.4%) and data transformation (45.6%); hallucinations were not observed with the multiagent approach.

Conclusions:

  • The developed multiagent AI framework shows superior accuracy and robustness in automating biomedical data analysis.
  • This specialized agent-based approach offers a significant advantage over generalized LLMs for complex medical data tasks.